Image Feature Binary Coding Representation Method Based on Point-Pair Relationship Learning and Reconstruction
A technology of binary coding and image features, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problems of high feature dimension and low retrieval efficiency, reduce storage requirements, improve performance and accuracy, The effect of low query efficiency
Active Publication Date: 2022-03-18
NANJING UNIV
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[0004] Purpose of the invention: The present invention aims at the problem of tight coupling between the hash function and the loss function of the optimization target in the existing unsupervised algorithm coding learning process, and proposes a loosely coupled image feature binary coding based on point pair relationship learning and reconstruction Representation method, so as to improve the performance and accuracy of image retrieval, effectively solve the problem of fast and accurate retrieval of images based on hash binary coded data
[0005] The image feature binary encoding representation method based on point-to-relationship learning and reconstruction constructed by the present invention aims to use machine learning and machine vision to reduce the high feature dimension and low retrieval efficiency in traditional image retrieval technology. In view of the problem of tight coupling between the hash function and the loss function of the optimization target in the encoding learning process of the existing unsupervised algorithm, the image retrieval performance can be improved by using the image feature binary encoding representation method based on point-to-relationship learning and reconstruction. and accuracy goals
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[0145] This embodiment includes the following parts:
[0146] Import 10 data, each data is 784-dimensional image data. Figure 5 The pictures for 10 data are shown.
[0147] After the point-to-relation learning steps (including solving the coefficient matrix, dictionary segmentation, and feature representation), the reconstructed representation of the original imported data can be obtained, that is, the learning of the point-to-relationship is completed.
[0148] The reconstruction of these 10 data is expressed as follows, that is, the input data of the next step point pair relationship reconstruction step:
0 0 0 0 0 1 0 0
0 0 0 0 0 0 1 0
0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 1
0.0397 0 0 0 0.9603 0 0 0
0.8445 0 0 0 0 0.1555 0 0
0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0
0.8739 0 0 0 0 0 0.1261 0
0 0.0534 0 0 0.9466 0 0 0
[0149] The next step is to reconstruct the point-to-point relationship, and the μ, B, W, and s variables will be updated iteratively.
[0150] For these...
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The invention discloses an image feature binary encoding representation method based on point-to-relationship learning and reconstruction, including: step 1, converting data into a dictionary representation form, solving constraint problems to obtain a coefficient matrix in the representation form; step 2, performing the step The coefficient matrix obtained in 1 is constructed to represent the weight matrix of the graph. On this graph, the dictionary items are divided into k groups by spectral clustering, so as to realize dictionary segmentation; step 3, given a new sample, calculate it in all dictionary items The reconstruction residual on the group, select the optimal dictionary item group corresponding to the smallest reconstruction residual to perform linear representation, so as to complete the learning of point-to-relationship; step 4, solve the optimal model, and learn to maintain the optimal Excellent binary encoding to realize the reconstruction of point-to-relationships.
Description
technical field [0001] The invention belongs to the field of image feature encoding, in particular to a binary encoding representation method of image features based on point-to-relationship learning and reconstruction. Background technique [0002] In today's era, with the rapid development of the Internet information age, the total amount of image data is also increasing rapidly. In the application of image retrieval, given a query image, the user needs to retrieve images similar to it from a large-scale database, and return the results according to the similarity ranking. For this application scenario, one of the most basic methods is: firstly extract features from the query image and the database image respectively. Then, the distance between the query image and each database image is calculated according to a certain metric (such as Euclidean distance). Finally, the database images are sorted according to the distance, and the top database images are returned as the r...
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IPC IPC(8): G06F16/583G06V10/762G06V10/772G06K9/62
CPCG06F18/2323G06F18/28
Inventor杨育彬甘元柱毛晓蛟
OwnerNANJING UNIV